parse = argparse.ArgumentParser()
parse.add_argument('-L', type=int)
args = parse.parse_args()
# dataset
wavs, labels, infos = theanoxla.datasets.load_freefield1010(subsample=2,
n_samples=7000)
wavs /= wavs.max(1, keepdims=True)
wavs_train, wavs_test, labels_train, labels_test = theanoxla.utils.train_test_split(
wavs, labels, 0.33)
# variables
L = args.L
BS = 6
signal = T.Placeholder((BS, len(wavs[0])), 'float32')
if L > 0:
WVD = T.signal.wvd(T.expand_dims(signal, 1), 1024, L=L, hop=32)
else:
WVD = T.signal.mfsc(T.expand_dims(signal, 1), 1024, 192, 80, 2, 44100 / 4,
44100 / 4)
tf_func = theanoxla.function(signal, outputs=[WVD], backend='cpu')
tf = T.Placeholder(WVD.shape, 'float32')
label = T.Placeholder((BS, ), 'int32')
deterministic = T.Placeholder((1, ), 'bool')
# first layer
NN = 32
import theanoxla.tensor as T
import matplotlib.pyplot as plt
from matplotlib import interactive
interactive(False)
# https://github.com/google/jax/blob/master/jax/lib/xla_bridge.py
from jax.lib import xla_client
from scipy.ndimage import gaussian_filter
fs, SIGNAL = read("output2.wav")
SIGNAL = SIGNAL[2**15:, 0]
SIGNAL = SIGNAL / SIGNAL.max()
SS = 2**16
signal = T.Placeholder((SS, ), "float32")
signal2 = T.reshape(signal, (1, 1, -1))
wv = T.signal.wvd(signal2, 1024, 32, L=32, apod=T.signal.hanning, mode="same")
sp = T.signal.spectrogram(signal2, 256, 32, apod=T.signal.hanning, mode="same")
melsp = T.signal.melspectrogram(signal2, 1024, 32, 80, 10, 20000, 22000)
mfcc = T.signal.mfcc(signal2, 1024, 32, 80, 10, 20000, 22000, 12)
filters = T.signal.mel_filterbank(1024, 80, 10, 20000, 22000)
fil = theanoxla.function(outputs=[filters])
tfs = theanoxla.function(signal,
outputs=[wv[0, 0], sp[0, 0], melsp[0, 0], mfcc[0, 0]])
t = time.time()
TFs = tfs(SIGNAL[:SS].astype("float32"))
FIL = fil()[0]
asdasd
w = T.Placeholder(SHAPE, 'float32', name='w')
noise = T.random.uniform(SHAPE, dtype='float32')
y = T.cos(theanoxla.nn.activations.leaky_relu(z,0.3) + w + noise)
cost = T.pool(y, (2, 2))
cost = T.sum(cost)
grads = theanoxla.gradients(cost, [w, z], [1])
print(cost.get({w: np.random.randn(*SHAPE)}))
noise.seed = 20
print(cost.get({w: np.random.randn(*SHAPE)}))
noise.seed = 40
print(cost.get({w: np.random.randn(*SHAPE)}))
updates = {z:z-0.01*grads[0]}
fn1 = theanoxla.function(w, outputs=[cost])
# KL div. between the prior (standard gaussian) and our distribution
enc = encoder(X, 2)
dec = decoder(enc, X.shape[1])
mu = enc[-1][:latent_dim]
logvar = enc[-1][latent_dim:]
divergence = -0.5 * (np.sum(T.exp(logvar) - logvar + mu**2, 1) - latent_dim)
rec = ((X - dec[-1])**2).sum(1)
elbo = tf.reduce_mean(rec - divergence)
loss = -elbo
BS = 100
lr = 0.001
DATA, _ = datasets.make_moons(1000)
X = T.Placeholder([BS, 2], 'float32')
Z = T.Placeholder([BS, 2], 'float32')
G_sample = generator(Z, 2)
logits = discriminator(T.concatenate([G_sample[-1], X]))
labels = T.concatenate([T.zeros(BS, dtype='int32'), T.ones(BS, dtype='int32')])
disc_loss = losses.sparse_crossentropy_logits(labels, logits[-1]).mean()
gen_loss = losses.sparse_crossentropy_logits(1 - labels[:BS],
logits[-1][:BS]).mean()
masks = T.concatenate([G_sample[1] > 0, G_sample[3] > 0], 1)
A = T.stack([
gradients(G_sample[-1][:, 0].sum(), [Z])[0],
gradients(G_sample[-1][:, 1].sum(), [Z])[0]
], 1)
parse.add_argument("-L", type=int)
args = parse.parse_args()
# dataset
wavs, labels, infos = theanoxla.datasets.load_freefield1010(subsample=2, n_samples=7000)
wavs /= wavs.max(1, keepdims=True)
wavs_train, wavs_test, labels_train, labels_test = theanoxla.utils.train_test_split(
wavs, labels, 0.33
)
# variables
L = args.L
BS = 6
signal = T.Placeholder((BS, len(wavs[0])), "float32")
if L > 0:
WVD = T.signal.wvd(T.expand_dims(signal, 1), 1024, L=L, hop=32)
else:
WVD = T.signal.mfsc(
T.expand_dims(signal, 1), 1024, 192, 80, 2, 44100 / 4, 44100 / 4
)
tf_func = theanoxla.function(signal, outputs=[WVD], backend="cpu")
tf = T.Placeholder(WVD.shape, "float32")
label = T.Placeholder((BS,), "int32")
deterministic = T.Placeholder((1,), "bool")
# first layer
import theanoxla
import theanoxla.tensor as T
import matplotlib.pyplot as plt
from matplotlib import interactive
interactive(False)
#https://github.com/google/jax/blob/master/jax/lib/xla_bridge.py
from jax.lib import xla_client
from scipy.ndimage import gaussian_filter
fs, SIGNAL = read('output2.wav')
SIGNAL = SIGNAL[2**15:, 0]
SIGNAL = SIGNAL / SIGNAL.max()
SS = 2**16
signal = T.Placeholder((SS, ), 'float32')
signal2 = T.reshape(signal, (1, 1, -1))
wv = T.signal.wvd(signal2, 1024, 32, L=32, apod=T.signal.hanning, mode='same')
sp = T.signal.spectrogram(signal2, 256, 32, apod=T.signal.hanning, mode='same')
melsp = T.signal.melspectrogram(signal2, 1024, 32, 80, 10, 20000, 22000)
mfcc = T.signal.mfcc(signal2, 1024, 32, 80, 10, 20000, 22000, 12)
filters = T.signal.mel_filterbank(1024, 80, 10, 20000, 22000)
fil = theanoxla.function(outputs=[filters])
tfs = theanoxla.function(signal,
outputs=[wv[0, 0], sp[0, 0], melsp[0, 0], mfcc[0, 0]])
t = time.time()
TFs = tfs(SIGNAL[:SS].astype('float32'))
FIL = fil()[0]
print(image)
print(image)
print(q)
asdf
T.signal.fft(image, axes=(-1, ))
print(image)
print(image.get({}))
#print(T.gather(image, [0, 1]))
#print(T.gather(image, [0, 1]).get({}))
#patches = T.extract_image_patches(image, (2, 1))
print(patches.shape)
print(patches.get({})[0, 0, 0, 0])
sdf
tr = T.Placeholder((10, 10), 'float32')
tr2 = T.concatenate([tr, tr], 0)
tr3 = tr * 4
print(tr2.roots, tr3.roots)
asdf
print(patches.get({}))
asdf
a = T.Placeholder((4, 4), 'float32')
print(a.roots)
b = a * 3 + a
c = b + b + a + b * a
print(c.roots)
asdf
import jax
import numpy as np
import sys
sys.path.insert(0, "../")
import theanoxla
import theanoxla.tensor as T
import theanoxla.nn as nn
w = T.Placeholder((3, ), np.float32, name='w')
# MAP example 1
output = T.map(lambda a, b: T.pow(a, b), w, T.cast(T.arange(3), 'float32'))
print(output.get({w: jax.numpy.arange(3).astype('float32')}))
fn = theanoxla.function(w, outputs=[output])
print(fn(jax.numpy.arange(3).astype('float32')))
# MAP example 2
output = T.map(lambda a: T.pow(a, 2.), T.cast(T.arange(3), 'float32'))
print(output.get())
fn = theanoxla.function(outputs=[output])
print(fn())
# SCAN example 1
output = T.scan(lambda a, b: a + b, T.zeros(1), T.reshape(w, (3, 1)))
print(output.get({w: jax.numpy.arange(3).astype('float32')}))
fn = theanoxla.function(w, outputs=[output])
print(fn(jax.numpy.arange(3).astype('float32')))
# SCAN example 2
output = T.scan(lambda a, b, c: a + b * c, T.zeros(1),
sys.path.insert(0, "../")
from scipy.io.wavfile import read
import theanoxla
import theanoxla.tensor as T
from theanoxla import layers
import matplotlib.pyplot as plt
from matplotlib import interactive
interactive(False)
#https://github.com/google/jax/blob/master/jax/lib/xla_bridge.py
from jax.lib import xla_client
from sklearn.metrics import roc_auc_score, accuracy_score
BS = 1
signal = T.Placeholder((BS, 4), 'float32')
deterministic = T.Placeholder((1, ), 'bool')
random = T.random.bernoulli((2, 2), p=0.5)
output = layers.Dropout(signal, 0.5, deterministic)
g = theanoxla.function(outputs=[random])
f = theanoxla.function(signal, deterministic, outputs=[output])
for epoch in range(100):
print(g())
for epoch in range(100):
print(f(np.ones((BS, 4)), 0)[0])
for epoch in range(100):
print(f(np.ones((BS, 4)), 1)[0])
# KL div. between the prior (standard gaussian) and our distribution
enc = encoder(X, 2)
dec = decoder(enc, X.shape[1])
mu = enc[-1][:latent_dim]
logvar = enc[-1][latent_dim:]
divergence = -0.5 * (np.sum(T.exp(logvar) - logvar + mu**2, 1) - latent_dim)
rec = ((X - dec[-1])**2).sum(1)
elbo = tf.reduce_mean(rec - divergence)
loss = -elbo
BS = 100
lr = 0.001
DATA, _ = datasets.make_moons(1000)
X = T.Placeholder([BS, 2], "float32")
Z = T.Placeholder([BS, 2], "float32")
G_sample = generator(Z, 2)
logits = discriminator(T.concatenate([G_sample[-1], X]))
labels = T.concatenate([T.zeros(BS, dtype="int32"), T.ones(BS, dtype="int32")])
disc_loss = losses.sparse_crossentropy_logits(labels, logits[-1]).mean()
gen_loss = losses.sparse_crossentropy_logits(1 - labels[:BS],
logits[-1][:BS]).mean()
masks = T.concatenate([G_sample[1] > 0, G_sample[3] > 0], 1)
A = T.stack(
[
gradients(G_sample[-1][:, 0].sum(), [Z])[0],
gradients(G_sample[-1][:, 1].sum(), [Z])[0],
from scipy.io.wavfile import read
import theanoxla
import theanoxla.tensor as T
from theanoxla import layers
import matplotlib.pyplot as plt
from matplotlib import interactive
interactive(False)
# https://github.com/google/jax/blob/master/jax/lib/xla_bridge.py
from jax.lib import xla_client
from sklearn.metrics import roc_auc_score, accuracy_score
BS = 1
signal = T.Placeholder((BS, 4), "float32")
deterministic = T.Placeholder((1, ), "bool")
random = T.random.bernoulli((2, 2), p=0.5)
output = layers.Dropout(signal, 0.5, deterministic)
g = theanoxla.function(outputs=[random])
f = theanoxla.function(signal, deterministic, outputs=[output])
for epoch in range(100):
print(g())
for epoch in range(100):
print(f(np.ones((BS, 4)), 0)[0])
for epoch in range(100):
print(f(np.ones((BS, 4)), 1)[0])
import jax
import numpy as np
import sys
sys.path.insert(0, "../")
import theanoxla
import theanoxla.tensor as T
image = T.Placeholder((512**2, ), 'float32')
output = image.reshape((1, 1, 512, 512))
f = theanoxla.function(image, outputs=[output])
for i in range(10000):
print(i)
f(np.random.randn(512**2))
import sys
sys.path.insert(0, "../")
import theanoxla
import theanoxla.tensor as T
def conv(x, y):
x = x[0, 0]
y = y[0, 0]
filter = y[::-1, ::-1]
return (x[:-1, :-1] * y[0, 0] + x[1:, :-1] * y[1, 0] +
x[:-1, 1:] * y[0, 1] + x[1:, 1:] * y[1, 1])
SHAPE = (1, 1, 1164, 1164)
w = T.Placeholder(SHAPE, "float32", name="w")
SHAPE2 = (1, 1, 2, 2)
filter = T.Placeholder(SHAPE2, "float32", name="filter")
output = T.convNd(w, filter)
f = theanoxla.function(w, filter, outputs=[output])
data = np.random.randn(*SHAPE).astype("float32")
filter = np.random.randn(*SHAPE2).astype("float32")
output = f(data, filter)[0][0, 0]
target = conv(data, filter)
print("% close values:",
100 * np.mean(np.isclose(target, output).astype("float32")))
import jax
import numpy as np
import sys
sys.path.insert(0, "../")
import theanoxla
import theanoxla.tensor as T
image = T.Placeholder((512**2, ), "float32")
output = image.reshape((1, 1, 512, 512))
f = theanoxla.function(image, outputs=[output])
for i in range(10000):
print(i)
f(np.random.randn(512**2))
print(image)
print(image)
print(q)
asdf
T.signal.fft(image, axes=(-1, ))
print(image)
print(image.get({}))
# print(T.gather(image, [0, 1]))
# print(T.gather(image, [0, 1]).get({}))
# patches = T.extract_image_patches(image, (2, 1))
print(patches.shape)
print(patches.get({})[0, 0, 0, 0])
sdf
tr = T.Placeholder((10, 10), "float32")
tr2 = T.concatenate([tr, tr], 0)
tr3 = tr * 4
print(tr2.roots, tr3.roots)
asdf
print(patches.get({}))
asdf
a = T.Placeholder((4, 4), "float32")
print(a.roots)
b = a * 3 + a
c = b + b + a + b * a
print(c.roots)
asdf
import argparse
parse = argparse.ArgumentParser()
parse.add_argument('-L', type=int)
args = parse.parse_args()
# dataset
wavs, labels, infos = theanoxla.datasets.load_freefield1010(subsample=2,
n_samples=7000)
# variables
L = args.L
BS = 1
signal = T.Placeholder((BS, wavs.shape[1]), 'float32')
if L > 0:
WVD = T.signal.wvd(T.expand_dims(signal, 1), 1024, L=L, hop=32)
else:
WVD = T.signal.mfsc(T.expand_dims(signal, 1), 1024, 192, 80, 2, 44100 / 4,
44100 / 4)
tf_func = theanoxla.function(signal, outputs=[WVD], backend='cpu')
# transform the data
for i, x in enumerate(
theanoxla.utils.batchify(wavs, batch_size=BS, option='continuous')):
print('before')
np.savez_compressed(